The Complexity of Large Language Models: A Marionette Analogy
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DALL·E 2024-05-01 23.07.11 - An artistic representation of a transformer model visualized as a marionette, depicting various components like tokenization, input embedding. Abstract: Large Language Models (LLMs) based on the transformer architecture have revolutionized the field of natural language processing (NLP). However, the intricate workings and complex components of these models can be challenging to understand. In this paper, I present a marionette analogy to help explain the key components and mechanisms of transformer-based LLMs. By mapping the elements of the transformer architecture to the components of a marionette performance, I hope to provide an accessible and engaging framework for understanding the operation of these models. The main body of the paper focuses on the core components and their marionette analogies, while the appendices offer in-depth discussions on specific relevant topics. Through this analogy, I hope to demystify the complexity of LLMs and highlight their potential in NLP applications.
The Complexity of Large Language Models: A Marionette Analogy
The Complexity of Large Language Models: A…
The Complexity of Large Language Models: A Marionette Analogy
DALL·E 2024-05-01 23.07.11 - An artistic representation of a transformer model visualized as a marionette, depicting various components like tokenization, input embedding. Abstract: Large Language Models (LLMs) based on the transformer architecture have revolutionized the field of natural language processing (NLP). However, the intricate workings and complex components of these models can be challenging to understand. In this paper, I present a marionette analogy to help explain the key components and mechanisms of transformer-based LLMs. By mapping the elements of the transformer architecture to the components of a marionette performance, I hope to provide an accessible and engaging framework for understanding the operation of these models. The main body of the paper focuses on the core components and their marionette analogies, while the appendices offer in-depth discussions on specific relevant topics. Through this analogy, I hope to demystify the complexity of LLMs and highlight their potential in NLP applications.